Archive

Frederick et.al. (2006), utilizing survey data primarily from readers at msnbc.com found a clear relationship (plotted in the image to the right) between BMI and body satisfaction. In this study, women tended to feel best about their bodies when their BMI was between 17.5-20 and men tended to feel best when their BMI was around 23-24. Using the standard rules-of-thumb for categorizing BMI values, women prefer being slightly underweight to on the low-side of normal weight. Men, on the other hand, prefer being at the higher end of normal weight. While no single illustration can accurately depict BMI (a range of heights/weights/ body types can produce identical BMI scores), the following images (available from BMI-Club) might be helpful.

Optimal BMI range for women’s self-rated body satisfaction (above).

Optimal BMI range for men’s self-rated body satisfaction (above).

Approximation of the point at which each sex’s BMI score predictably results in a “My body is unattractive” self-evaluation (above).

Approximation of the non-optimal BMI level for both women’s and men’s self-rated body satisfaction (above).

Noteworthy observations:

men are more satisfied with larger BMI scores (not surprising)

optimal BMI scores for both sexes do not predictably produce an “I have a good body” self-evaluation in either sex

Cautions

Conclusions based on the data used in this study may not generalize well to the larger population.

MSNBC.com is the most popular news site on the web; the age-distribution of web news consumers is improving, but is still skewed toward the young and the educated, sex differences exist in preferences for what types of news are pursued, partisan political/ideological differences result in use of different news sources; etc. (Pew Research Center Report)

a recent review comparing self-report with objective measures of height and weight found a trend of under-reporting for weight and over-reporting for height in self-reports; with significant levels of variation between studies and widely divergent methods of measuring or estimating height/weight (doi: 10.1111/j.1467-789X.2007.00347.x)

for women the mean BMI in this study is 24.2 while the mean self-reported BMI in the NHANES study is 27.2 (and the mean measured BMI in the same study is 28.0)

for men the mean BMI in this study is 26.6 while the mean self-reported BMI in the NHANES study is 27.6 (and the measured BMI in the same study is 28.0)

studies with large sample sizes show a near zero relationship between attractiveness ratings and asymmetry F1,36=6.97 (p=.01)

Visible vs Not Visible Asymmetries

The distinction here is straightforward: if raters can see the measured asymmetry it is assumed that the asymmetry is visible. For example, if facial asymmetries are measured and faces are evaluated for attractiveness, then the study is categorized as visible. On the other hand, if the asymmetry is in the body and photos of faces are rated, the study is categorized as not visible. This visible/not visible distinction is relevant to why humans find symmetry attractive – for example in comparing a good genes interpretation of symmetry to a processing fluency interpretation. Van Dongen makes the point that the models underlying the good genes interpretation of the role of symmetry on attractiveness require that non-observable symmetry be significantly related to attractiveness ratings.

Publication Bias

Since the value of a comprehensive data analysis is only as good as the data it uses, a check on the quality of the included sources is highly desirable. One such check is a funnel plot. In a funnel plot, each study is plotted for effect size and sample size. Since variation resulting from chance is more likely to be larger in studies with small sample sizes, a visual inspection of the plotted data points for the included studies should show a symmetric distribution around the typical effect size, with more variation in effect size expected for studies using smaller samples (hence the name, funnel plot). The published studies exploring the relationship between attractiveness and symmetry do not reveal the expected funnel-like symmetric shape. While a number of causes for this undesirable result are possible, publication bias is the most likely. A statistical technique used to minimize the effect of publication bias is the trim and fill method (especially useful in cases of publication bias). Van Dongen uses this technique to more accurately estimate the actual effect size of symmetry on beauty ratings.

Sample Size

A meta analysis that shows a decreasing effect size in studies with increasing sample sizes is another indicator of publication bias. The rationale for this conclusion is that there is a preference for publishing research that has found support for a particular hypothesis over research that reports finding no relationship. A manuscript that supports the null hypothesis is typically more interesting when the sample size is larger and thus gets published. Manuscripts that do not show a relationship are left ‘in the file drawer.’

Conclusion

Van Dongen’s overall study results, after accounting for bias, found that there was a significant effect of visible asymmetry on visual attractiveness ratings (r=.15 with a 95% confidence interval of 0.07-0.23). This degree of effect size is typically categorized as small/medium. What that means is that a person of average attractiveness (left side of the highlighted area below – the 50th percentile) who suddenly became more symmetric (by the typical amount of variation in symmetry found in human faces naturally) would now be rated more attractive than 62 percent of other people (the right side of he highlighted area below).

Another way to illustrate this degree of change is via a beauty rating scale. For ease of estimation, imagine an 8-point rating scale (from 0-8) that is normally distributed with 4 as the average, typical score. This degree of change would take the average person’s rating from a 4 to a 4.3. On this same scale, a person who is in all other respects rated average for attractiveness, but who had an exceptionally high degree of symmetry, would likely be rated a 5 rather than a 4 (this degree of symmetry is expected only in about 1/1000 people. It is important to note that for the illustrations above to hold, the changes in symmetry must be visible. Van Dongen’s meta analysis found no relationship between attractiveness ratings and the symmetry measures of features that are not visible to the person doing the rating.

Cautions

One significant limitation in the symmetry/attractiveness literature is that the data primarily come from western, college student samples – limiting our ability to generalize these conclusions to other populations.

You may hear about a study that suggests the attractiveness of your avatar in a virtual world influences how you’ll be treated. If you’d like to read the full-text of the study by Banakou & Chorianopoulis (2010) you can access the article here. Richard Landers has a nice summary and discussion of the article here – if you’d like a second opinion.

I’ll add a few cautions to the information contained in the above links:

the article contains a number of assertions followed by the following statement in bold text: “Error! Reference source not found” – I’ve not seen this before in a published article but I do find it a bit concerning.

Appearance matters – but not in the same way for male and female political candidates. Limiting this discussion to the beauty-relevant elements of Chiao, Bowman, & Gill’s 2008 study, being attractive has an effect on rates of voting for female candidates while appearing approachable has an effect on women’s rates of voting for male candidates (in a laboratory simulation). It is important to note that no other information about each candidate was given to the laboratory rater/voters.

Since the photographs were of actual candidates in U.S. Congressional elections, the authors were able to also assess how ratings of attractiveness and approachability of faces in photographs in the lab related to real-world election results. Using this metric, female candidates’ attractiveness had a much smaller effect overall (r=.09*); as did male candidates’ approachability for women (r=.18*). Appearance does seem to matter for political candidates, but, appearance is not the only thing voters attend to when voting.

Strengths

Stimuli were of actual political candidates

Limitations

Subjects were undergraduate students at Northwestern University

Interestingly, in the actual congressional elections, laboratory ratings of how competent and dominant faces appeared correlated with a candidate getting elected.

I’ve begun reading Geoffrey Jones‘ (the Isidor Straus Professor of Business History at Harvard University) new book, Beauty Imagined. Rather than writing a traditional review, I’ll post comments about some of the information most relevant to the psychology of beauty as I read through the book. To start, humans spend $330 billion dollars a year (world-wide) on fragrances, cosmetics, and toiletries. Assuming 501 billion cups of coffee are consumed worldwide, it appears we spend about as much money on f,c & t’s as we do on coffee. But, rounding up, NATO spends close to 3x and the United States spends close to 2x that on military expenditures. L’Oreal is the world’s biggest beauty company and Procter & Gamble is number two. Avon is the biggest brand name, followed by Dove, then Pantene. There is significant variation in the types of beauty products that are consumed in different regions/nations:

Europeans, proportionally, spend more on fragrance and skin care than Americans

Americans likewise spend more on make-up than do Europeans

The Asian-Pacific region accounts for 6% of the fragrance market and 40% of the skin care market

China’s skin-care market is 4x the size of its make-up market

Skin-lightening products are significant in Asian markets, but not elsewhere